Cancer stem cell, niche and EGFR decide tumor development and treatment response: A bio-computational simulation study

Nanoparticle-mediated cancer cell therapy: basic science to clinical applications

Every sixth person in the world dies due to cancer, making it the second leading severe cause of death after cardiovascular diseases. According to WHO, cancer claimed nearly 10 million deaths in 2020. The most common types of cancers reported have been breast (lung, colon and rectum, prostate cases), skin (non-melanoma) and stomach. In addition to surgery, the most widely used traditional types of anti-cancer treatment are radio- and chemotherapy. However, these do not distinguish between normal and malignant cells. Additional treatment methods have evolved over time for early detection and targeted therapy of cancer. However, each method has its limitations and the associated treatment costs are quite high with adverse effects on the quality of life of patients. Use of individual atoms or a cluster of atoms (nanoparticles) can cause a paradigm shift by virtue of providing point of sight sensing and diagnosis of cancer. Nanoparticles (1-100 nm in size) are 1000 times smaller in size than the human cell and endowed with safer relocation capability to attack mechanically and chemically at a precise location which is one avenue that can be used to destroy cancer cells precisely. This review summarises the extant understanding and the work done in this area to pave the way for physicians to accelerate the use of hybrid mode of treatments by leveraging the use of various nanoparticles.

Growth dynamics of breast cancer stem cells: effects of self-feedback and EMT mechanisms

Breast cancer stem cells (BCSCs) with the ability to self-renew and differentiate have been identified in primary breast cancer tissues and cell lines. The BCSCs are often resistant to traditional radiation and/or chemotherapies. Previous studies have also shown that successful therapy must eradicate cancer stem cells. The purpose of this paper is to develop a mathematical model with self-feedback mechanism to illustrate the issues regarding the difficulties of absolutely eliminating a breast cancer. In addition, we introduce the mechanism of the epithelial-mesenchymal transition (EMT) to investigate the influence of EMT on the effects of breast cancer growth and treatment. Results indicate that the EMT mechanism facilitates the growth of breast cancer and makes breast cancer more difficult to be cured. Therefore, targeting the signals involved in EMT can halt tumor progression in breast cancer. Finally, we apply the experimental data to carry out numerical simulations and validate our theoretical conclusions.

Reconstruction of tissue-specific genome-scale metabolic models for human cancer stem cells

Cancer Stem Cells (CSCs) contribute to cancer aggressiveness, metastasis, chemo/radio-therapy resistance, and tumor recurrence. Recent studies emphasized the importance of metabolic reprogramming of CSCs for the maintenance and progression of the cancer phenotype through both the fulfillment of the energetic requirements and the supply of substrates fundamental for fast-cell growth, as well as through metabolite-induced epigenetic regulation. Therefore, it is of paramount importance to develop therapeutic strategies tailored to target the metabolism of CSCs. In this work, we built computational Genome-Scale Metabolic Models (GSMMs) for CSCs of different tissues. Flux simulations were then used to predict metabolic phenotypes, identify potential therapeutic targets, and spot already-known Transcription Factors (TFs), miRNAs and antimetabolites that could be used as part of drug repurposing strategies against cancer. Results were in accordance with experimental evidence, provided insights of new metabolic mechanisms for already known agents, and allowed for the identification of potential new targets and compounds that could be interesting for further in vitro and in vivo validation.

Optimization of Cancer Treatment in the Frequency Domain

Thorough exploration of alternative dosing frequencies is often not performed in conventional pharmacometrics approaches. Quantitative systems pharmacology (QSP) can provide novel insights into optimal dosing regimen and drug behaviors which could add a new dimension to the design of novel treatments. However, methods for such an approach are currently lacking. Recently, we illustrated the utility of frequency-domain response analysis (FdRA), an analytical method used in control engineering, using several generic pharmacokinetic-pharmacodynamic case studies. While FdRA is not applicable to models harboring ever increasing variables such as those describing tumor growth, studying such models in the frequency domain provides valuable insight into optimal dosing frequencies. Through the analysis of three distinct tumor growth models (cell cycle-specific, metronomic, and acquired resistance), we demonstrate the application of a simulation-based analysis in the frequency domain to optimize cancer treatments. We study the response of tumor growth to dosing frequencies while simultaneously examining treatment safety, and found for all three models that above a certain dosing frequency, tumor size is insensitive to an increase in dosing frequency, e.g., for the cell cycle-specific model, one dose per 3 days, and an hourly dose yield the same reduction of tumor size to 3% of the initial size after 1 year of treatment. Additionally, we explore the effect of drug elimination rate changes on the tumor growth response. In summary, we show that the frequency-domain view of three models of tumor growth dynamics can help in optimizing drug dosing regimen to improve treatment success.

A function-blocking CD47 antibody suppresses stem cell and EGF signaling in triple-negative breast cancer

CD47 is a signaling receptor for thrombospondin-1 and the counter-receptor for signal-regulatory protein-α (SIRPα). By inducing inhibitory SIRPα signaling, elevated CD47 expression by some cancers prevents macrophage phagocytosis. The anti-human CD47 antibody B6H12 inhibits tumor growth in several xenograft models, presumably by preventing SIRPα engagement. However, CD47 signaling in nontransformed and some malignant cells regulates self-renewal, suggesting that CD47 antibodies may therapeutically target cancer stem cells (CSCs). Treatment of MDA-MB-231 breast CSCs with B6H12 decreased proliferation and asymmetric cell division. Similar effects were observed in T47D CSCs but not in MCF7 breast carcinoma or MCF10A breast epithelial cells. Gene expression analysis in breast CSCs treated with B6H12 showed decreased expression of epidermal growth factor receptor (EGFR) and the stem cell transcription factor KLF4. EGFR and KLF4 mRNAs are known targets of microRNA-7, and B6H12 treatment correspondingly enhanced microRNA-7 expression in breast CSCs. B6H12 treatment also acutely inhibited EGF-induced EGFR tyrosine phosphorylation. Expression of B6H12-responsive genes correlated with CD47 mRNA expression in human breast cancers, suggesting that the CD47 signaling pathways identified in breast CSCs are functional in vivo. These data reveal a novel SIRPα-independent mechanism by which therapeutic CD47 antibodies could control tumor growth by autonomously forcing differentiation of CSC.

CD44(+)/CD24(-) breast cancer cells exhibit phenotypic reversion in three-dimensional self-assembling peptide RADA16 nanofiber scaffold

Background

Self-assembling peptide nanofiber scaffolds have been shown to be a permissive biological material for tissue repair, cell proliferation, differentiation, etc. Recently, a subpopulation (CD44⁺/CD24⁻) of breast cancer cells has been reported to have stem/progenitor cell properties. The aim of this study was to investigate whether this subpopulation of cancer cells have different phenotypes in self-assembling COCH₃-RADARADARADARADA-CONH₂ (RADA16) peptide nanofiber scaffold compared with Matrigel^® (BD Biosciences, Two Oak Park, Bedford, MA, USA) and collagen I.

Methods

CD44 and CD24 expression was determined by flow cytometry. Cell proliferation was measured by 5-bromo-2′-deoxyuridine assay and DNA content measurement. Immunostaining was used to indicate the morphologies of cells in three-dimensional (3D) cultures of different scaffolds and the localization of β-catenin in the colonies. Western blot was used to determine the expression of signaling proteins. In vitro migration assay and inoculation into nude mice were used to evaluate invasion and tumorigenesis in vivo.

Results

The breast cancer cell line MDA-MB-435S contained a high percentage (>99%) of CD44⁺/CD24⁻ cells, which exhibited phenotypic reversion in 3D RADA16 nanofiber scaffold compared with collagen I and Matrigel. The newly formed reverted acini-like colonies reassembled a basement membrane and reorganized their cytoskeletons. At the same time, cells cultured and embedded in RADA16 peptide scaffold exhibited growth arrest. Also, they exhibited different migration potential, which links their migration ability with their cellular morphology. Consistent with studies in vitro, the in vivo tumor formation assay further supported of the functional changes caused by the reversion in 3D RADA16 culture. Expression levels of intercellular surface adhesion molecule-1 were upregulated in cells cultured in RADA16 scaffolds, and the NF-kappa B inhibitor pyrrolidine dithiocarbamate could inhibit RADA16-induced upregulation of intercellular surface adhesion molecule-1 and the phenotype reversion of MDA-MB-453S cells.

Conclusion

Culturing a CD44⁺/CD24⁻-enriched breast cancer cell population in 3D RADA16 peptide nanofiber scaffold led to a significant phenotypic reversion compared with Matrigel and collagen I.

A mathematical-biological joint effort to investigate the tumor-initiating ability of Cancer Stem Cells

The involvement of Cancer Stem Cells (CSCs) in tumor progression and tumor recurrence is one of the most studied subjects in current cancer research. The CSC hypothesis states that cancer cell populations are characterized by a hierarchical structure that affects cancer progression. Due to the complex dynamics involving CSCs and the other cancer cell subpopulations, a robust theory explaining their action has not been established yet. Some indications can be obtained by combining mathematical modeling and experimental data to understand tumor dynamics and to generate new experimental hypotheses. Here, we present a model describing the initial phase of ErbB2⁺ mammary cancer progression, which arises from a joint effort combing mathematical modeling and cancer biology. The proposed model represents a new approach to investigate the CSC-driven tumorigenesis and to analyze the relations among crucial events involving cancer cell subpopulations. Using in vivo and in vitro data we tuned the model to reproduce the initial dynamics of cancer growth, and we used its solution to characterize observed cancer progression with respect to mutual CSC and progenitor cell variation. The model was also used to investigate which association occurs among cell phenotypes when specific cell markers are considered. Finally, we found various correlations among model parameters which cannot be directly inferred from the available biological data and these dependencies were used to characterize the dynamics of cancer subpopulations during the initial phase of ErbB2⁺ mammary cancer progression.

Nonlinear growth kinetics of breast cancer stem cells: implications for cancer stem cell targeted therapy

Cancer stem cells (CSCs) have been identified in primary breast cancer tissues and cell lines. The CSC population varies widely among cancerous tissues and cell lines, and is often associated with aggressive breast cancers. Despite of intensive research, how the CSC population is regulated within a tumor is still not well understood so far. In this paper, we present a mathematical model to explore the growth kinetics of CSC population both in vitro and in vivo. Our mathematical models and supporting experiments suggest that there exist non-linear growth kinetics of CSCs and negative feedback mechanisms to control the balance between the population of CSCs and that of non-stem cancer cells. The model predictions can help us explain a few long-standing questions in the field of cancer stem cell research, and can be potentially used to predict the efficicacy of anti-cancer therapy.

Targeting the biophysical properties of the myeloma initiating cell niches: a pharmaceutical synergism analysis using multi-scale agent-based modeling

Multiple myeloma, the second most common hematological cancer, is currently incurable due to refractory disease relapse and development of multiple drug resistance. We and others recently established the biophysical model that myeloma initiating (stem) cells (MICs) trigger the stiffening of their niches via SDF-1/CXCR4 paracrine; The stiffened niches then promote the colonogenesis of MICs and protect them from drug treatment. In this work we examined in silico the pharmaceutical potential of targeting MIC niche stiffness to facilitate cytotoxic chemotherapies. We first established a multi-scale agent-based model using the Markov Chain Monte Carlo approach to recapitulate the niche stiffness centric, pro-oncogenetic positive feedback loop between MICs and myeloma-associated bone marrow stromal cells (MBMSCs), and investigated the effects of such intercellular chemo-physical communications on myeloma development. Then we used AMD3100 (to interrupt the interactions between MICs and their stroma) and Bortezomib (a recently developed novel therapeutic agent) as representative drugs to examine if the biophysical properties of myeloma niches are drugable. Results showed that our model recaptured the key experimental observation that the MBMSCs were more sensitive to SDF-1 secreted by MICs, and provided stiffer niches for these initiating cells and promoted their proliferation and drug resistance. Drug synergism analysis suggested that AMD3100 treatment undermined the capability of MICs to modulate the bone marrow microenvironment, and thus re-sensitized myeloma to Bortezomib treatments. This work is also the first attempt to virtually visualize in 3D the dynamics of the bone marrow stiffness during myeloma development. In summary, we established a multi-scale model to facilitate the translation of the niche-stiffness centric myeloma model as well as experimental observations to possible clinical applications. We concluded that targeting the biophysical properties of stem cell niches is of high clinical potential since it may re-sensitize tumor initiating cells to chemotherapies and reduce risks of cancer relapse.

Dynamics between cancer cell subpopulations reveals a model coordinating with both hierarchical and stochastic concepts

Tumors are often heterogeneous in which tumor cells of different phenotypes have distinct properties. For scientific and clinical interests, it is of fundamental importance to understand their properties and the dynamic variations among different phenotypes, specifically under radio- and/or chemo-therapy. Currently there are two controversial models describing tumor heterogeneity, the cancer stem cell (CSC) model and the stochastic model. To clarify the controversy, we measured probabilities of different division types and transitions of cells via in situ immunofluorescence. Based on the experiment data, we constructed a model that combines the CSC with the stochastic concepts, showing the existence of both distinctive CSC subpopulations and the stochastic transitions from NSCCs to CSCs. The results showed that the dynamic variations between CSCs and non-stem cancer cells (NSCCs) can be simulated with the model. Further studies also showed that the model can be used to describe the dynamics of the two subpopulations after radiation treatment. More importantly, analysis demonstrated that the experimental detectable equilibrium CSC proportion can be achieved only when the stochastic transitions from NSCCs to CSCs occur, indicating that tumor heterogeneity may exist in a model coordinating with both the CSC and the stochastic concepts. The mathematic model based on experimental parameters may contribute to a better understanding of the tumor heterogeneity, and provide references on the dynamics of CSC subpopulation during radiotherapy.

N-cadherin impedes proliferation of the multiple myeloma cancer stem cells

Multiple myeloma (MM) is an incurable malignancy of the plasma cells localized to the bone marrow. A rare population of MM cancer stem cells (MM-CSCs) has been shown to be responsible for maintaining the pull of residual disease and to contribute to myeloma relapse. The stem cells are found in a bone marrow niche in contact with the stromal cells that are responsible for maintaining the proliferative quiescence of the MM-CSC and regulate its self-renewal and differentiation decisions. Here we show that both MM and bone marrow stromal cells express N-cadherin, a cell-cell adhesion molecule shown to maintain a pool of leukemic stem cells. Inhibition of N-cadherin using a neutralizing antibody led to an increase in the MM cell proliferation. A decrease in MM cell adhesion to the bone marrow stroma was observed in the first 24 hours of co-culture followed by a 2.3-30-fold expansion of the adherent cells. Moreover, inhibition of N-cadherin led to a 4.8-9.6-fold expansion of the MM-CSC population. Surprisingly, addition of the N-cadherin antagonist peptide resulted in massive death of the non-adherent MM cells, while the viability of the adherent cells and MM-CSCs remained unaffected. Interestingly, the proliferative effects of N-cadherin inhibition were not mediated by the nuclear translocation of β-catenin. Taken together, our findings demonstrate the crucial role of N-cadherin in regulating MM cell proliferation and viability and open an interesting avenue of investigation to understand how structural modifications of N-cadherin can affect MM cell behavior. Our findings suggest that targeting N-cadherin may be a useful therapeutic strategy to treat MM in conjunction with an agent that has anti-MM-CSC activity.

A 3D multiscale model of cancer stem cell in tumor development

Background

Recent reports indicate that a subgroup of tumor cells named cancer stem cells (CSCs) or tumor initiating cells (TICs) are responsible for tumor initiation, growth and drug resistance. This subgroup of tumor cells has self-renewal capacity and could differentiate into heterogeneous tumor cell populations through asymmetric proliferation. The idea of CSC provides informative insights into tumor initiation, metastasis and treatment. However, the underlying mechanisms of CSCs regulating tumor behaviors are unclear due to the complex cancer system. To study the functions of CSCs in the complex tumor system, a few mathematical modeling studies have been proposed. Whereas, the effect of microenvironment (mE) factors, the behaviors of CSCs, progenitor tumor cells (PCs) and differentiated tumor cells (TCs), and the impact of CSC fraction and signaling heterogeneity, are not adequately explored yet.

Methods

In this study, a novel 3D multi-scale mathematical modeling is proposed to investigate the behaviors of CSCsin tumor progressions. The model integrates CSCs, PCs, and TCs together with a few essential mE factors. With this model, we simulated and investigated the tumor development and drug response under different CSC content and heterogeneity.

Results

The simulation results shown that the fraction of CSCs plays a critical role in driving the tumor progression and drug resistance. It is also showed that the pure chemo-drug treatment was not a successful treatment, as it resulted in a significant increase of the CSC fraction. It further shown that the self-renew heterogeneity of the initial CSC population is a cause of the heterogeneity of the derived tumors in terms of the CSC fraction and response to drug treatments.

Conclusions

The proposed 3D multi-scale model provides a new tool for investigating the behaviors of CSC in CSC-initiated tumors, which enables scientists to investigate and generate testable hypotheses about CSCs in tumor development and drug response under different microenvironments and drug perturbations.

The relationship of cancer stem cells in urological cancers

Numerous studies are ongoing to identify and isolate cancer stem cells from cancers of genito-urinary tracts. Better understanding of their role in prostate, urothelial and kidney cancer origin, growth and progression opens new pathways in development of more effective treatment methods. However there are still many issues before advances in this field can be introduced for clinical application. This review addresses current achievements in cancer stem cells research in uro-oncology.

Application of circuit simulation method for differential modeling of TIM-2 iron uptake and metabolism in mouse kidney cells

Circuit simulation is a powerful methodology to generate differential mathematical models. Due to its highly accurate modeling capability, circuit simulation can be used to investigate interactions between the parts and processes of a cellular system. Circuit simulation has become a core technology for the field of electrical engineering, but its application in biology has not yet been fully realized. As a case study for evaluating the more advanced features of a circuit simulation tool called Advanced Design System (ADS), we collected and modeled laboratory data for iron metabolism in mouse kidney cells for a H ferritin (HFt) receptor, T cell immunoglobulin and mucin domain-2 (TIM-2). The internal controlling parameters of TIM-2 associated iron metabolism were extracted and the ratios of iron movement among cellular compartments were quantified by ADS. The differential model processed by circuit simulation demonstrated a capability to identify variables and predict outcomes that could not be readily measured by in vitro experiments. For example, an initial rate of uptake of iron-loaded HFt (Fe-HFt) was 2.17 pmol per million cells. TIM-2 binding probability with Fe-HFt was 16.6%. An average of 8.5 min was required for the complex of TIM-2 and Fe-HFt to form an endosome. The endosome containing HFt lasted roughly 2 h. At the end of endocytosis, about 28% HFt remained intact and the rest was degraded. Iron released from degraded HFt was in the labile iron pool (LIP) and stimulated the generation of endogenous HFt for new storage. Both experimental data and the model showed that TIM-2 was not involved in the process of iron export. The extracted internal controlling parameters successfully captured the complexity of TIM-2 pathway and the use of circuit simulation-based modeling across a wider range of cellular systems is the next step for validating the significance and utility of this method.

Microenvironment for cancer stem cells

Cancer stem cells and their metastatic niche are one of hot topics for tumor study. This review introduces the definition of microenvironment (niche) for cancer stem cells, related cells and factors, characteristics and regulation of niche, premetastatic niche and tissue components. The research progress in this field can provide some clues to the metastatic mechanism of tumors and the development and improvement of chemotherapeutic drugs.

Cancer stem cell theory: therapeutic implications for nanomedicine

Evidence continues to accumulate showing that tumors contain a minority population of cells responsible for tumor initiation, growth, and recurrence. These are termed "cancer stem cells" (CSCs). Functional assays have identified the self-renewal and tumor-initiation capabilities of CSCs. Moreover, recent studies have revealed that these CSCs is responsible for chemotherapy resistance within a tumor. Several mechanisms of chemoresistance have been proposed, including increased Wnt/β-catenin and Notch signaling, as well as high expression levels of adenosine triphosphate-binding cassette transporters, an active DNA repair capacity, and slow rate of self-renewal. Nanoscale drug-delivery systems, which transport therapeutically active molecules, prolong circulation, and improve biodistribution in the body, may allow more effective and specific therapies to address the challenges posed by CSCs. In particular, some nanovehicles are being exploited for selective drug delivery to CSCs and show promising results. In this review, we highlight the mechanisms of drug resistance and the novel strategies using nanoscale drugs to eliminate CSCs.

Targeting tumor-infiltrating macrophages decreases tumor-initiating cells, relieves immunosuppression, and improves chemotherapeutic responses

Tumor-infiltrating immune cells can promote chemoresistance and metastatic spread in aggressive tumors. Consequently, the type and quality of immune responses present in the neoplastic stroma are highly predictive of patient outcome in several cancer types. In addition to host immune responses, intrinsic tumor cell activities that mimic stem cell properties have been linked to chemoresistance, metastatic dissemination, and the induction of immune suppression. Cancer stem cells are far from a static cell population; rather, their presence seems to be controlled by highly dynamic processes that are dependent on cues from the tumor stroma. However, the impact immune responses have on tumor stem cell differentiation or expansion is not well understood. In this study, we show that targeting tumor-infiltrating macrophages (TAM) and inflammatory monocytes by inhibiting either the myeloid cell receptors colony-stimulating factor-1 receptor (CSF1R) or chemokine (C-C motif) receptor 2 (CCR2) decreases the number of tumor-initiating cells (TIC) in pancreatic tumors. Targeting CCR2 or CSF1R improves chemotherapeutic efficacy, inhibits metastasis, and increases antitumor T-cell responses. Tumor-educated macrophages also directly enhanced the tumor-initiating capacity of pancreatic tumor cells by activating the transcription factor STAT3, thereby facilitating macrophage-mediated suppression of CD8(+) T lymphocytes. Together, our findings show how targeting TAMs can effectively overcome therapeutic resistance mediated by TICs.

Multi-scale agent-based brain cancer modeling and prediction of TKI treatment response: incorporating EGFR signaling pathway and angiogenesis

Background

The epidermal growth factor receptor (EGFR) signaling pathway and angiogenesis in brain cancer act as an engine for tumor initiation, expansion and response to therapy. Since the existing literature does not have any models that investigate the impact of both angiogenesis and molecular signaling pathways on treatment, we propose a novel multi-scale, agent-based computational model that includes both angiogenesis and EGFR modules to study the response of brain cancer under tyrosine kinase inhibitors (TKIs) treatment.

Results

The novel angiogenesis module integrated into the agent-based tumor model is based on a set of reaction–diffusion equations that describe the spatio-temporal evolution of the distributions of micro-environmental factors such as glucose, oxygen, TGFα, VEGF and fibronectin. These molecular species regulate tumor growth during angiogenesis. Each tumor cell is equipped with an EGFR signaling pathway linked to a cell-cycle pathway to determine its phenotype. EGFR TKIs are delivered through the blood vessels of tumor microvasculature and the response to treatment is studied.

Conclusions

Our simulations demonstrated that entire tumor growth profile is a collective behaviour of cells regulated by the EGFR signaling pathway and the cell cycle. We also found that angiogenesis has a dual effect under TKI treatment: on one hand, through neo-vasculature TKIs are delivered to decrease tumor invasion; on the other hand, the neo-vasculature can transport glucose and oxygen to tumor cells to maintain their metabolism, which results in an increase of cell survival rate in the late simulation stages.

An Integrated Multiscale Mechanistic Model for Cancer Drug Therapy

In this paper, we established a multiscale mechanistic model for studying drug delivery, biodistribution, and therapeutic effects of cancer drug therapy in order to identify optimal treatment strategies. Due to the specific characteristics of cancer, our proposed model focuses on drug effects on malignant solid tumor and specific internal organs as well as the intratumoral and regional extracellular microenvironments. At the organ level, we quantified drug delivery based on a multicompartmental model. This model will facilitate the analysis and prediction of organ toxicity and provide important pharmacokinetic information with regard to drug clearance rates. For the analysis of intratumoral microenvironment which is directly related to blood drug concentrations and tumor properties, we constructed a drug distribution model using diffusion-convection solute transport to study temporal/spatial variations of drug concentration. With this information, our model incorporates signaling pathways for the analysis of antitumor response with drug combinations at the extracellular level. Moreover, changes in tumor size, cellular proliferation, and apoptosis induced by different drug treatment conditions are studied. Therefore, the proposed multi-scale model could be used to understand drug clinical actions, study drug therapy-antitumor effects, and potentially identify optimal combination drug therapy. Numerical simulations demonstrate the proposed system's effectiveness.

[Targeted therapies and radiation therapy in non-small cell lung cancer]

Lung cancer is the leading cause of cancer-related death. Between 80-85% of lung cancers are non-small cell lung carcinomas. One third of the patients are diagnosed with locally advanced stage. In this condition, concomitant radio-chemotherapy is the standard treatment for patients with good performance status. Despite important improvements in the last years, non-small cell lung carcinoma prognosis remains poor, with high rates of both local recurrences and metastases. The heterogeneity of molecular characteristics of non-small cell lung carcinoma cells and a better knowledge of potential targets offer promising developments for new pharmacologic agents. Hereafter we will review the currently most studied pathways and the most promising ones for the treatment of locally advanced unresectable non-small cell lung carcinoma. Two of the most attractive pathways where new agents have been developed and assessed in combination with thoracic radiotherapy or radiochemotherapy are the EGFR pathway (either with the use of monoclonal antibodies or tyrosine kinase inhibitors) and the angiogenesis inhibition. The development of targeted agents could lead to individualized therapeutic combinations taking into account the intrinsic characteristics of tumor cells. Pharmacological modulation of tumour cells radiosensitivity by targeted therapies is only starting, but yet offers promising perspectives.

[Cancer stem cells: a new target for lung cancer treatment]

Lung cancer remains the leading cause of cancer death. Understanding lung tumours physiopathology should provide opportunity to prevent tumour development or/and improve their therapeutic management. Cancer stem cell theory refers to a subpopulation of cancer cells also named tumour initiating cells that can drive cancer development. Cells presenting these characteristics have been identified and isolated from lung cancer. Exploring cell markers and signalling pathways specific to lung cancer stem cells may lead to progress in therapy and improve the prognosis of patients with lung cancer. Continuous efforts in developing in vitro and in vivo models may yield reliable tools to better understand cancer stem cell abilities and to test new therapeutic targets. Even if some data are in favour of a higher chemo and radioresistance of cancer stem cells this issue remains disputed. Preclinical data on putative cancer stem cell targets are emerging by now. These preliminary studies are critical for the next generation of lung cancer therapies.

Lung cancer stem cell: new insights on experimental models and preclinical data

Lung cancer remains the leading cause of cancer death. Understanding lung tumors physiopathology should provide opportunity to prevent tumor development or/and improve their therapeutic management. Cancer stem cell (CSC) theory refers to a subpopulation of cancer cells, also named tumor-initiating cells, that can drive cancer development. Cells presenting these characteristics have been identified and isolated from lung cancer. Exploring cell markers and signaling pathways specific to lung CSCs may lead to progress in therapy and improve the prognosis of patients with lung cancer. Continuous efforts in developing in vitro and in vivo models may yield reliable tools to better understand CSC abilities and to test new therapeutic targets. Preclinical data on putative CSC targets are emerging by now. These preliminary studies are critical for the next generation of lung cancer therapies.